HetroTraffSim: A Macroscopic Heterogeneous Traffic Flow Simulator for Road Bottlenecks
Bibliographic record
Abstract
Smart mobility is crucial for future smart cities. Traffic simulation software (TSS) is an important tool for efficient planning and management of road networks to achieve this goal. Many TSS tools have been developed for both microscopic and macroscopic homogenous traffic flow. However, only two (SUMO and HetroSim) are applicable to heterogeneous traffic. In this paper, HetroTraffSim is proposed to simulate macroscopic heterogeneous traffic flows at road bottlenecks. It is developed using the Unity3D engine and is based on a second-order traffic flow model. It is evaluated for a 360 m road segment on University Road, Peshawar, Pakistan. This segment contains a 78.5 m bottleneck which causes traffic congestion. This bottleneck is due to the construction of a pedestrian overhead bridge which reduces the three-lane road to two lanes. HetroTraffSim provides normalized traffic velocity, average traffic density, traffic flow, and time, as well as the temporal and spatial evolution of traffic. The results obtained show that a change in the distance headway affects the traffic flow, velocity, and density. Further, HetroTraffSim can be extended to automated traffic flows using raycasting. It can easily be used to create realistic traffic scenarios and the computational complexity is low due to the small-degree polynomials employed. HetroTraffSim can be used by traffic planners to improve traffic flow and public safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".